Training Data Sources and Fluid Intelligence

Discussing Prompt Engineering and recent OpenAI developments with ex-OpenAI Creative Apps and Scientific Communicator Andrew Mayne

Timestamps:

00:00:00 - Teaser Reel Intro

00:01:01 - Intro / Andrew's background

00:02:49 - What was it like working at OpenAI when you first joined?

00:12:59 - Was Andrew basically one of the earliest Prompt Engineers?

00:14:04 - How Andrew Hacked his way into a tech job at OpenAI

00:17:08 - Parallels between Hollywood and Tech jobs

00:20:58 - Parallels between the world of Magic and working at OpenAI

00:25:00 - What was OpenAI like in the Early Days?

00:30:24 - Why it was hard promoting GPT-3 early on

00:31:00 - How would you describe the current 'instruction age' of prompt design?

00:35:22 - What was GPT-4 like freshly trained?

00:39:00 - Is there anything different about the raw base model without RLHF?

00:42:00 - Optimizations that go into Language models like GPT-4

00:43:30 - What was it like using DALL-E 3 very early on?

00:44:38 - Do you know who came up with the 'armchair in the shape of an avocado' prompt at OpenAI?

00:45:48 - Did you experience 'DALL-E Dreams' as a part of the DALL-E 2 beta?

00:47:16 - How else has prompt design changed?

00:49:27 - How has prompt design changed because of ChatGPT?

00:52:40 - How to get ChatGPT to mimick and emulate personalities better?

00:54:30 - Mimicking Personalities II (How to do Style with ChatGPT)

00:56:40 - Fine Tuning ChatGPT for Mimicking Elon Musk

00:59:44 - How do you get ChatGPT to come up with novel and brilliant ideas?

01:02:40 - How do you get ChatGPt to get away from conventional answers?

01:05:14 - Will we ever get single-shot, real true novelty from LLM's?

01:10:05 - Prompting for ChatGPT Voice Mode

01:12:20 - Possibilities and Prompting for GPT-4 Vision

01:15:45 - GPT-4 Vision Use Cases/Startup Ideas

01:21:37 - Does multimodality make language models better or are the benefits marginal?

01:24:00 - Intuitively, has multimodality improved the world model of LLM's like GPT-4?

01:25:33 - What would it take for ChatGPT to write half of your next book?

01:29:10 - Qualitatively, what would it take to convince you about a book written by AI?  What are the characteristics?

01:31:30 - Could an LLM mimick Andrew Mayne's writing style?

01:37:49 - Jailbreaking ChatGPT

01:41:12 - What's the next era of prompt engineering?

01:45:50 - How have custom instructions changed the game?

01:54:41 - How far do you think we are from asking a model how to make 10 million dollars and getting back a legit answer?

02:01:07 - Part II - Making Money with LLM's

02:11:32 - How do you make a chat bot more reliable and safe?

02:12:12 - How do you get ChatGPT to consistently remember criteria and work within constraints?

02:12:45 - What about DALL-E?  How do you get it to better create within constraints?

02:14:14 - What's your prompt practice like?

02:15:10 - Do you intentionally sit down and practice writing prompts?

02:16:45 - How do you build an intuition around prompt design for an LLM?

02:20:00 - How do you like to iterate on prompts? Do you have a process?

02:21:45 - How do you know when you've hit the ceiling with a prompt?

02:24:00 - How do you know a single line prompt is has room to improve?

02:26:40 - Do you actually need to know OpenAI's training data?  What are some ways to mitigate this?

02:30:40 - What are your thoughts on automated prompt writing/optimization?

02:33:20 - How do you get a job as a prompt engineer?  What makes a top tier prompt engineer different from an everyday user?

02:37:20 - How do you think about scaling laws a prompt engineer?

02:39:00 - Effortless Prompt Design

02:40:52 - What are some research areas that would get you a job at OpenAI?

02:43:30 - The Research Possibilities of Optimization & Inference

02:45:59 - If you had to guess future capabilities of GPT-5 what would they be?

02:50:16 - What are some capabilities that got trained out of GPT-4 for ChatGPT?

02:51:10 - Is there any specific capability you could imagine for GPT-5?  Why is it so hard to predict them?

02:56:06 - Why is it hard to predict future LLM capabilities? (Part II)

02:59:47 - What made you want to leave OpenAI and start your own consulting practice?

03:05:29 - Any remaining advice for creatives, entrepreneurs, prompt engineers?

03:09:25 - Closing

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